Impacts of shared mobility on vehicle lifetimes and on the carbon footprint of electric vehicles

Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles—the additional distance traveled to pick up passengers—may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

-Another problem when the authors try to incorporate the relationship found in conventional vehicles to shared AVs is that as people are expecting longer driving range for AVs, so the lifetime design for AVs could be very different from conventional vehicles. In that circumstance, I am afraid that the analysis could incorporate significant bias.
- Figure 1, I would recommend the authors to expand the explanations on this figure. More information should be provided, for example, the relationship between lifetime and lifetime driving distance; driving distance and lifetime driving distance.
-To use the relationship obtained on ICEVs on BEVs could incorporate biases, so that I would recommend the authors to conducted some sensitivity analysis.
-If the authors have already determine the value of e to be -0.59, does it make a lot of senses to analyze the cases of e equaling to 0 and 1? -the empty travel section, all the discussions are very hard to follow due to the complication to the analysis. I recommend the authors to simply the discussion and highlight the most important findings in an easy-to-follow way.
-Line 241, yes higher driving intensity leads to shorter vehicle lifetime, which makes the use-phase GHG emissions because more emissions occur in the near future than in the long future. However, this analysis is only from one-vehicle perspective. But when considering that after the retirement of this vehicle, the next vehicle will get the benefit of lower emissions of electricity in the long future, than the near-future loss could be filled.
-line 243, the analysis on the manufacturing-phase GHG emissions actually need also to incorporate the impact from electricity emission factor, as much of energy consumption for battery production is electricity. This will lead to some differences in the manufacturing phase emissions, like the impact from near-term emission factor and long-term emissions factor analyzed in the use-phase emissions.
-line 257, the authors should not only identify the research weakness, actually this can be addressed by a sensitivity analysis, which I recommend that the authors should add.
Reviewer #3: Remarks to the Author: Summary of contribution: This paper contributes meaningfully to the literature on vehicle lifetime carbon footprints by using retirement statistics to model relationships between driving intensity and lifetime, and by using those relationships to characterize whether high driving intensities tend to improve or harm carbon footprints. Sensitivity to an appropriate range of models is considered. The value of these findings largely comes from its implications for "future mobility", e.g., ridesourcing, car sharing, and autonomous vehicles. The dataset used is generally appropriate to investigate those implications and provides a much-needed loook at intensity-lifetime relationships.
That said, it isn't clear to me whether the dataset can yield findings that cleanly extrapolate to those "future mobility" options --this may be something that simply warrants an additional caveat in the discussion section but seems important to acknowledge. It also isn't clear to me whether there are specific insights that could translate these vehicle-level findings to fleet-level findings (are fleets of very few high-use cars better or worse than fleets of very many low-use cars?) --maybe nothing can be said on that front, but maybe there are at least some specific directions the authors can suggest as future work based on their model findings.
Comments on data and methods: 1. The study depends on using historical vehicle retirements to infer trends for future (shared) mobility. It seems like one missing piece of the discussion section is whether this subsetted excerpt of all passenger cars may or may not cleanly extrapolate to uses such as car sharing and ride sourcing. Should we expect that the vehicle failure causes and reasons for retirement will be pretty much the same for shared mobility fleets as for all private vehicles? Or is there anything about their driving cycles (mostly long-duration urban driving shifts), ongoing maintenance regimes, or scrappage decisions (made by professional fleet managers instead of individual owners) that might make the data not extrapolate as cleanly such that results are biased in a direction we can characterize? This may be worth some brief comment. 2. I am glad to see multiple statistical model types tested for the semi-empirical model. The specific tradeoffs between model types (Table 1) are outside of my expertise, but the explanations provided were clear and concise. Including elasticities of 0 and -1 in the main figures helped me understand how the model is working and also serves as a useful bound. 3. This analysis is conducted and presented on a per-vehicle basis. I might hesitate to draw fleetwide conclusions from this analysis, and I think it should be mentioned in the problem framing or in discussion that the interactions between average vehicle use level and fleetwide impacts are outside of the paper's scope. For example, if demand is fixed and served 100% via AVs and ridesourcing, each unit increase in vehicle driving intensity seems to imply fewer total cars are needed --but I don't think this analysis can tell us whether the net carbon impacts of that increase in intensity would be positive or negative.
Additional comments: 1. Please comment on the choice to use average electricity grid emissions factors instead of marginal and how this choice may affect your results and findings. In particular, as renewables increase, average electricity emissions factors will fall, but marginal emissions factors may or may not change (in nearly all regions the marginal generator is some form of fossil fuel generation). Might using marginal factors alter any findings? If so, it is worth some justification in the text of this choice. 2. It seems that AVs, ridesourcing, and/or car sharing may cause not only empty vehicle travel, but also additional induced demand (ie, the reduced costs or inconveniences of travel due to these additional options may lead to new trip generation). An interesting complement to the sensitivity analysis of breakeven points for deadheading would be a similar look at breakevens for percent of increased vehicle-distance traveled due to new travel demand. This may be out of scope for this

REVIEWER COMMENTS
We would like to thank both reviewers for taking the time to thoroughly review our study. Their comments have greatly improved the manuscript. Please find our point-by-point response to all the comments below. Changes have been made in the corresponding areas in the manuscript. All line references are in regards to the revised manuscript. Text highlighted in yellow in citations show what part of the text that was revised.

Reviewer #1 (Remarks to the Author):
Dear authors, This is a very interesting study investigating the relationship between vehicle use intensity, lifetime, the implications of shared mobility, and the carbon footprint. I think there is potential for this piece of study to be accepted for publication. But intensive revisions are needed.
-Generally, this study seems very like a combination of three pieces of small studies, which are (1) the relationship between vehicle use intensity, lifetime and lifetime driving distance, which is obtained by using conventional ICEV as the example.
(2) a carbon footprint calculation for BEV with the previous relationships in (1) incorporated.
(3) a calculation on the empty travel that can be tolerated. I would say theses three parts of the study are not very closely integrated with each other. It is more like a loose combination of researches in three different research areas, and trying to find some stories behind the combination. I recommend that the authors might put far more focus on the first part of the study, because currently there lacks understanding in the relationship between driving intensity and vehicle lifetime. And the first part could be not necessarily connected to the BEV carbon footprint and shared AV empty mileage contexts.
Thank you for pointing out that the three parts do not seem closely integrated. We have thoroughly revised the manuscript to improve the coherence of the study and, also added a more thorough analysis of the relationship between driving intensity and vehicle lifetime.
We agree that there is a lack of understanding of the relationship between driving intensity and vehicle lifetime. At the same time, there is a strong trend towards electrification both by many car manufacturers and through the ICEV phase-out policies proposed by several governments worldwide. Furthermore, car sharing and ride sharing are to some extent already implemented (e.g., ride hailing) and thoroughly discussed in academia as well as industry, including in the latest assessment from WG3 of the IPCC. Hence, a study trying to understand the relationship between driving intensity and vehicle lifetime to inform studies of future scenarios for transportation and policy analyses should relate them to the potential issues with electrification and potential future shared AVs. That is the reasoning behind our study's objective and why the three different parts are all vital to the study.
-Another problem when the authors try to incorporate the relationship found in conventional vehicles to shared AVs is that as people are expecting longer driving range for AVs, so the lifetime design for AVs could be very different from conventional vehicles. In that circumstance, I am afraid that the analysis could incorporate significant bias.
Thank you for highlighting this problem. We acknowledge the risk of biases when using data for ICEVs as the basis for discussing the relationship between driving intensity and vehicle lifetime. Although we agree that there is a risk with using data based on ICEVs to analyze the impact of an emerging technology, we argue that both the design of future regular/autonomous BEVs and to what extent longer driving ranges would affect the relationship are highly uncertain. Even if enough data for a statistical analysis of EVs were available, the risk of bias would still persist. To highlight this, the following discussions has been added in the first section of the manuscript (lines 134-167): "Currently, battery degradation is often raised as a constrain to the cumulative driving distance and lifetime of BEVs 28-30 , but the BEV is a relatively new technology on the market and, hence, statistics on battery lifetimes from real-world driving are scarce. The number of electric vehicles on the world's roads were in the thousands in 2010 and grew rapidly to reach about 2 million by 2016 and over 10 million by 2020 31,32 . Hence, if enough retirement statistics for electric vehicles were available to make thorough statistical analyses, most vehicles would be much less than 10 years old. However, the limited data currently available on cars with batteries in Swedish vehicle retirement statistics show similar distributions as the stratified data presented above, see Supplementary Notes 1-3 and Supplementary Figures 11-12. However, the data show shorter lifetimes on average (due to the limited historic data on electrified vehicles) and with a bias towards hybrid electric vehicles (HEVs) due to very few BEVs and plug-in hybrid electric vehicles (PHEVs) having been retired during the analyzed period. Many BEV manufacturers already have warranties for their batteries of about seven to eight years or about 150,000 to 240,000 km, whichever comes first 33-37 . Future battery chemistries may further reduce degradation. Some studies suggest that future batteries may have significantly longer lifetimes than today through completely different battery chemistries 38 , changes in charging and use behavior 39 , and/or changed battery design 40 that could potentially yield a cumulative driving distance of more than three million kilometers -effectively outliving the vehicle. These improvements, if they materialize, would likely improve the cycling of the batteries. However, other factors could still limit the vehicle's lifetime 25 , such as accidents, aging of other vehicle parts (e.g., structural elements of chassis and body), economic reasons and consumer trends. Further, the durability of the vehicle is significantly dependent on the vehicle design, material selection and business models 41 .
In summary, the results suggest that the annual driving intensity indeed has a strong influence on vehicle lifetimes. The relationship between driving intensity and vehicle lifetime may differ between BEVs and ICEVs, but not enough data is yet available to make such a claim. As a consequence, the remainder of this article explores how changes in annual driving intensity may influence the carbon footprint of passenger car travel, assuming that the relationship shown for ICEVs is applicable as a proxy for individually owned and shared autonomous BEVs. We capture the uncertainty in future vehicle lifetimes of (shared and autonomous) BEVs by highlighting extreme values for the relationship between annual driving intensity and vehicles lifetime as well as the empirically estimated relationship based on ICEV retirement data." And the following in the discussion section (lines 376-387): "Finally, our conclusions rely on the assumption that the relationship between driving intensity and vehicle lifetime established in the semi-empirical model will hold also for future regular and autonomous BEVs. In this article, we present preliminary evidence suggesting that cars with batteries follow similar trends as ICEVs, but the design and use of future batteries and vehicles are still highly uncertain. Hence, the intention here is to highlight potential consequences based on currently available data and discuss them in relation to extreme cases. Those extreme cases highlight a range of plausible outcomes if the lifetime characteristics of future batteries and vehicles may deviate from those of current passenger cars. In any case, the analysis shows that the carbon footprint may be substantially reduced if the relationship between average annual driving intensity and vehicle lifetime is weakened, pointing to the importance of designing future BEVs (both autonomous and regular) for durability." - Figure 1, I would recommend the authors to expand the explanations on this figure. More information should be provided, for example, the relationship between lifetime and lifetime driving distance; driving distance and lifetime driving distance.
Thank you for this suggestion. Figure 1 has been revised to visualize not only the relationship between driving intensity and vehicle lifetime but also vehicle lifetime vs. total driving distance and total driving distance vs driving intensity. The following analysis is also added (lines 102-133: "The stratification is made for individual average annual driving intensity classes, varying from 0 to 100,000 km per year in steps of 10,000 km per year. For each individual driving intensity class, a close to linear relationship exists between vehicle lifetime and cumulative driving distance. The linear slope becomes steeper with each higher driving intensity class, see Figure 1a. This suggests that the calendar age of a vehicle becomes generally shorter with increasing annual driving intensity. Further, the cumulative driving distances are distributed across a wide range for higher driving intensity classes, see Figure 1c, while the distribution is narrower for lower driving intensities. Hence, the probability of a retirement decision at a specific cumulative driving distance becomes smaller as the annual driving intensity increases. A fixed cumulative driving distance is assumed in many lifecycle assessments of vehicles 13,18 . However, this assumption is not corroborated by the data presented here. Finally, the distribution of vehicle lifetimes becomes narrower and shifts towards lower vehicle lifetimes as the average driving intensity increases, see Figure 1b. Hence, we focus the following analysis on empirically describing the relationship between driving intensity and vehicle lifetime in order to capture the impact of vehicle use on retirement age. The average vehicle lifetime decreases with each higher driving intensity class, from 19 years for average driving intensities of 0-10,000 km per year to 3.9 years for average driving intensities of 90,001-100,000 km per year, see Figure 1b. The standard deviation of the distributions also indicates that the range of probable lifetimes becomes narrower with increasing annual driving intensity (although the standard deviation increases in relative terms). The standard deviation decreases from 5.0 years for driving intensities of 0-10,000 km per year to 1.9 years for driving intensities of 90,001-100,000 km per year (assuming Normaldistributed data). Results for a categorization in four vehicle sizes (mini, medium, large and luxury size cars, see Supplementary Figure 5) suggest that cars with low annual driving intensity are mainly represented by small size cars, while large to luxury size cars mainly have higher annual driving intensities. Medium size cars cover the full spectrum of annual driving intensities." -To use the relationship obtained on ICEVs on BEVs could incorporate biases, so that I would recommend the authors to conducted some sensitivity analysis.
-If the authors have already determine the value of e to be -0.59, does it make a lot of senses to analyze the cases of e equaling to 0 and 1?
Thanks again for highlighting the risk of bias in the dataset. We have added a visualization (Supplementary Figure 11) of the data points available for cars with batteries in the analyzed dataset. Although the dataset is too limited for a thorough statistical analysis, the available data points follow similar distributions as the data analyzed for ICEVs. This is thoroughly discussed in Supplementary Notes 1-3. The following discussion has also been added to the main body of the manuscript (lines 134-157), as previously mentioned.
"Currently, battery degradation is often raised as a constrain to the cumulative driving distance and lifetime of BEVs 28-30 , but the BEV is a relatively new technology on the market and, hence, statistics on battery lifetimes from real-world driving are scarce. The number of electric vehicles on the world's roads were in the thousands in 2010 and grew rapidly to reach about 2 million by 2016 and over 10 million by 2020 31,32 . Hence, if enough retirement statistics for electric vehicles were available to make thorough statistical analyses, most vehicles would be much less than 10 years old. However, the limited data currently available on cars with batteries in Swedish vehicle retirement statistics show similar distributions as the stratified data presented above, see Supplementary Notes 1-3 and Supplementary Figures 11-12. However, the data show shorter lifetimes on average (due to the limited historic data on electrified vehicles) and with a bias towards hybrid electric vehicles (HEVs) due to very few BEVs and plug-in hybrid electric vehicles (PHEVs) having been retired during the analyzed period.
Many BEV manufacturers already have warranties for their batteries of about seven to eight years or about 150,000 to 240,000 km, whichever comes first 33-37 . Future battery chemistries may further reduce degradation. Some studies suggest that future batteries may have significantly longer lifetimes than today through completely different battery chemistries 38 , changes in charging and use behavior 39 , and/or changed battery design 40 that could potentially yield a cumulative driving distance of more than three million kilometers -effectively outliving the vehicle. These improvements, if they materialize, would likely improve the cycling of the batteries. However, other factors could still limit the vehicle's lifetime 25 , such as accidents, aging of other vehicle parts (e.g., structural elements of chassis and body), economic reasons and consumer trends. Further, the durability of the vehicle is significantly dependent on the vehicle design, material selection and business models 41 ." Finally, we would also like to highlight the benefit of presenting results for elasticities ranging from 0 to -1 as a way of testing the sensitivity in the carbon footprint estimations as well as the empty travel breakeven level. Including these two extreme cases serves two purposes: (i) increasing the understanding of the model design, and (ii) how sensitive the model is to the relationship between driving intensity and vehicle lifetime. Hence, if future shared autonomous BEVs are designed in a way where the driving intensity plays a less important role in the decision to retire vehicles, the results are more likely related to an elasticity close to 0. This could be the case if battery degradation is less influenced by going through many charging cycles. The opposite case, where calendar lifetime plays a less important role in the decision to retire vehicles and the elasticity is close to -1, represents a future where batteries are largely impacted by the number of charging cycles and the total driving distance is fixed. The latter assumption is often used in LCA studies, in which a certain total driving distance over the vehicle's lifetime is assumed. However, the elasticity of -1 case seems less realistic given the incentives for battery manufacturers to improve battery longevity and enable batteries to cope with extreme events such as fast charging and recent laboratory studies supporting that such battery chemistries are feasible (Yang et al., 2021). We highlight this benefit of the extreme cases more clearly in the manuscript (lines 182-194): "Carbon footprints are also estimated for two extreme cases, ε = 0 and ε = -1, representing no influence of driving intensity on lifetime and full influence of driving intensity, respectively. The two extreme cases show the sensitivity of the model design to the assumed elasticity. The range represents possible cases if the model was trained on different retirement data, such as future BEVs when sufficient data becomes available. ε = 0 is a relevant extreme case if future individually owned and/or shared autonomous BEVs are designed in a way where driving intensity has no importance in the decision to retire vehicles. This could be the case if the vehicle and battery degradation is only influenced by calendar age. ε = -1 represent a case where vehicle aging, including aging of the battery, is only dependent on distance driven (i.e., battery aging only depends on the number of charging cycles). This approach is used in many lifecycle assessments 13,18 , where fixed cumulative vehicles distances are assumed. Note though that the elasticity affecting the distribution is based on the empirical data (β ≈ 0.51) also for the extreme cases." We also note that Reviewer #3 consider the analysis of the extreme values for the elasticity as a strength of the study.
-the empty travel section, all the discussions are very hard to follow due to the complication to the analysis. I recommend the authors to simply the discussion and highlight the most important findings in an easy-to-follow way.
We are sorry that you found this section hard to follow and agree that it is complex. We have thoroughly reworked this section in the revised manuscript and highlighted the main outcome that now is based on the fleet-wide analysis.
-Line 241, yes higher driving intensity leads to shorter vehicle lifetime, which makes the use-phase GHG emissions because more emissions occur in the near future than in the long future. However, this analysis is only from one-vehicle perspective. But when considering that after the retirement of this vehicle, the next vehicle will get the benefit of lower emissions of electricity in the long future, than the near-future loss could be filled.
Thank you for this suggestion. We agree that modelling a fleet would be a more accurate way of determining the impact of shared autonomous BEVs on the carbon footprint. Hence, we have reworked the sections on the carbon footprint impacts (lines 168-261) and the breakeven level for empty travel (lines 262-339) using a simple vehicle fleet turnover simulation considering a fleet of 1000 vehicles. The carbon footprint estimation section of the Methods section was also revised to include the vehicle fleet turnover simulation (lines 498-588). The carbon footprint estimation section was also moved to the end of the Methods section to follow the structure of the manuscript in general. Since these two sections and the related section in Methods are fully reworked, we have not included the whole text here.
We would also like to highlight that implementing our semi-empirical lifetime-intensity model in a fleet-wide analysis revealed additional aspects of the model that are important to consider when implementing the full distributions. Using the elasticity design with Normal distributions is simple and easy to understand but has a vital flaw when analyzing high driving intensities. As driving intensities increase, the distribution shifts to lower and even negative lifetimes, which of course is not realistic. This issue is overcome by instead using Weibull distributions, which is by definition always larger than zero. Hence, we have changed the elasticities used in the carbon footprint analysis to those estimated for the Weibull distribution and added the caveat of the Normal distribution to Table 1. Again, thank you for suggesting us to go beyond the one-vehicle perspective, which highlighted this important aspect of our lifetime-intensity model design.
-line 243, the analysis on the manufacturing-phase GHG emissions actually need also to incorporate the impact from electricity emission factor, as much of energy consumption for battery production is electricity. This will lead to some differences in the manufacturing phase emissions, like the impact from near-term emission factor and long-term emissions factor analyzed in the use-phase emissions.
Thank you for highlighting that this was not clear in the previous version. The model does incorporate the impact of the electricity emission factor on emissions related to battery production. All vehicles and batteries are assumed to be produced by global average manufacturing industries, using average global electricity. The electricity emission factor is based on direct emissions estimated by the IEA and adjusted to account for upstream processes, as described in the Methods section. The Methods highlights this on the following lines (546-549 and 574-577), included below for your convenience.
"Manufacturing-phase CO2 emissions are estimated for car sales in each year based on manufacturing processes as implemented in GREET® for the Stated Policies Scenario, while new and innovative processes are phased in over time for the Sustainable Development Scenario based on a literature review 4 ." "For the global electricity mix used in manufacturing and for charging, future direct emissions and adjustments to account for transmission and distribution losses (based on the difference between estimated supply and demand) are based on estimates by the IEA 56 for the two decarbonization pathways, Stated Policies and Sustainable Development."

-line 257, the authors should not only identify the research weakness, actually this can be addressed by a sensitivity analysis, which I recommend that the authors should add.
Thanks again for highlighting these issues. We hope that the analyses and discussions added, and outlined above, are sufficient to address this main weakness of our study.

Reviewer #3 (Remarks to the Author):
Summary of contribution: This paper contributes meaningfully to the literature on vehicle lifetime carbon footprints by using retirement statistics to model relationships between driving intensity and lifetime, and by using those relationships to characterize whether high driving intensities tend to improve or harm carbon footprints. Sensitivity to an appropriate range of models is considered. The value of these findings largely comes from its implications for "future mobility", e.g., ridesourcing, car sharing, and autonomous vehicles. The dataset used is generally appropriate to investigate those implications and provides a much-needed loook at intensity-lifetime relationships.
That said, it isn't clear to me whether the dataset can yield findings that cleanly extrapolate to those "future mobility" options --this may be something that simply warrants an additional caveat in the discussion section but seems important to acknowledge. It also isn't clear to me whether there are specific insights that could translate these vehicle-level findings to fleet-level findings (are fleets of very few high-use cars better or worse than fleets of very many low-use cars?) --maybe nothing can be said on that front, but maybe there are at least some specific directions the authors can suggest as future work based on their model findings.
Thank you for taking the time to thoroughly review our manuscript. We have thoroughly revised the manuscript in response to the comments, including replacing the previous carbon footprint analysis with one based on vehicle fleet turnover simulations as well as more thorough discussions on the applicability of the results for future mobility options. The latter also includes insights from the available but limited data on vehicle retirement of cars with batteries.
Comments on data and methods: 1. The study depends on using historical vehicle retirements to infer trends for future (shared) mobility. It seems like one missing piece of the discussion section is whether this subsetted excerpt of all passenger cars may or may not cleanly extrapolate to uses such as car sharing and ride sourcing. Should we expect that the vehicle failure causes and reasons for retirement will be pretty much the same for shared mobility fleets as for all private vehicles? Or is there anything about their driving cycles (mostly long-duration urban driving shifts), ongoing maintenance regimes, or scrappage decisions (made by professional fleet managers instead of individual owners) that might make the data not extrapolate as cleanly such that results are biased in a direction we can characterize? This may be worth some brief comment.
Thank you for highlighting this problem. We acknowledge the risk of biases when using data for ICEVs as the basis for discussing the relationship between driving intensity and vehicle lifetime. Although we agree that there is a risk with using data based on ICEVs to analyze the impact of an emerging technology, we argue that both the design of future regular/autonomous BEVs and to what extent longer driving ranges would affect the relationship are highly uncertain. Even if enough data for a statistical analysis of EVs were available, the risk of bias would still persist. To highlight this, the following discussions has been added in the first section of the manuscript (lines 134-167): "Currently, battery degradation is often raised as a constrain to the cumulative driving distance and lifetime of BEVs 28-30 , but the BEV is a relatively new technology on the market and, hence, statistics on battery lifetimes from real-world driving are scarce. The number of electric vehicles on the world's roads were in the thousands in 2010 and grew rapidly to reach about 2 million by 2016 and over 10 million by 2020 31,32 . Hence, if enough retirement statistics for electric vehicles were available to make thorough statistical analyses, most vehicles would be much less than 10 years old. However, the limited data currently available on cars with batteries in Swedish vehicle retirement statistics show similar distributions as the stratified data presented above, see Supplementary Notes 1-3 and Supplementary Figures 11-12. However, the data show shorter lifetimes on average (due to the limited historic data on electrified vehicles) and with a bias towards hybrid electric vehicles (HEVs) due to very few BEVs and plug-in hybrid electric vehicles (PHEVs) having been retired during the analyzed period.
Many BEV manufacturers already have warranties for their batteries of about seven to eight years or about 150,000 to 240,000 km, whichever comes first 33-37 . Future battery chemistries may further reduce degradation. Some studies suggest that future batteries may have significantly longer lifetimes than today through completely different battery chemistries 38 , changes in charging and use behavior 39 , and/or changed battery design 40 that could potentially yield a cumulative driving distance of more than three million kilometers -effectively outliving the vehicle. These improvements, if they materialize, would likely improve the cycling of the batteries. However, other factors could still limit the vehicle's lifetime 25 , such as accidents, aging of other vehicle parts (e.g., structural elements of chassis and body), economic reasons and consumer trends. Further, the durability of the vehicle is significantly dependent on the vehicle design, material selection and business models 41 .
In summary, the results suggest that the annual driving intensity indeed has a strong influence on vehicle lifetimes. The relationship between driving intensity and vehicle lifetime may differ between BEVs and ICEVs, but not enough data is yet available to make such a claim. As a consequence, the remainder of this article explores how changes in annual driving intensity may influence the carbon footprint of passenger car travel, assuming that the relationship shown for ICEVs is applicable as a proxy for individually owned and shared autonomous BEVs. We capture the uncertainty in future vehicle lifetimes of (shared and autonomous) BEVs by highlighting extreme values for the relationship between annual driving intensity and vehicles lifetime as well as the empirically estimated relationship based on ICEV retirement data." And the following in the discussion section (lines 376-387): "Finally, our conclusions rely on the assumption that the relationship between driving intensity and vehicle lifetime established in the semi-empirical model will hold also for future regular and autonomous BEVs. In this article, we present preliminary evidence suggesting that cars with batteries follow similar trends as ICEVs, but the design and use of future batteries and vehicles are still highly uncertain. Hence, the intention here is to highlight potential consequences based on currently available data and discuss them in relation to extreme cases. Those extreme cases highlight a range of plausible outcomes if the lifetime characteristics of future batteries and vehicles may deviate from those of current passenger cars. In any case, the analysis shows that the carbon footprint may be substantially reduced if the relationship between average annual driving intensity and vehicle lifetime is weakened, pointing to the importance of designing future BEVs (both autonomous and regular) for durability." 2. I am glad to see multiple statistical model types tested for the semi-empirical model. The specific tradeoffs between model types (Table 1)  Thank you! We agree that highlighting the extreme values for the elasticities in the figures improves understanding of the model and how sensitive it is to the relationship between driving intensity and vehicle lifetime. Hence, if future shared AVs are designed in a way where the driving intensity plays a less important role in the decision to retire vehicles, the results are more likely related to an elasticity close to 0. This could be the case if battery degradation is less influenced by going through many charging cycles. The opposite case, where vehicle lifetime plays a less important role in the decision to retire vehicles and the elasticity is close to -1, represents a future where batteries are largely impacted by the number of charging cycles.
We have highlighted this benefit more clearly in the manuscript (lines 182-194): "Carbon footprints are also estimated for two extreme cases, ε = 0 and ε = -1, representing no influence of driving intensity on lifetime and full influence of driving intensity, respectively. The two extreme cases show the sensitivity of the model design to the assumed elasticity. The range represents possible cases if the model was trained on different retirement data, such as future BEVs when sufficient data becomes available. ε = 0 is a relevant extreme case if future individually owned and/or shared autonomous BEVs are designed in a way where driving intensity has no importance in the decision to retire vehicles. This could be the case if the vehicle and battery degradation is only influenced by calendar age. ε = -1 represent a case where vehicle aging, including aging of the battery, is only dependent on distance driven (i.e., battery aging only depends on the number of charging cycles). This approach is used in many lifecycle assessments 13,18 , where fixed cumulative vehicles distances are assumed. Note though that the elasticity affecting the distribution is based on the empirical data (β ≈ 0.51) also for the extreme cases." 3. This analysis is conducted and presented on a per-vehicle basis. I might hesitate to draw fleetwide conclusions from this analysis, and I think it should be mentioned in the problem framing or in discussion that the interactions between average vehicle use level and fleetwide impacts are outside of the paper's scope. For example, if demand is fixed and served 100% via AVs and ridesourcing, each unit increase in vehicle driving intensity seems to imply fewer total cars are needed --but I don't think this analysis can tell us whether the net carbon impacts of that increase in intensity would be positive or negative.
Thank you for this suggestion. We agree that modelling a fleet would be a more accurate way of determining the impact of shared autonomous BEVs on the carbon footprint. Hence, we have reworked the sections on the carbon footprint impacts (lines 168-261) and the breakeven level for empty travel (lines 262-339) using a simple vehicle fleet turnover simulation considering a fleet of 1000 vehicles. The carbon footprint estimation section of the Methods section was also revised to include the vehicle fleet turnover simulation (lines 498-588). The carbon footprint estimation section was also moved to the end of the Methods section to follow the structure of the manuscript in general. Since these two sections and the related section in Methods are fully reworked, we have not included the whole text here.
We would also like to highlight that implementing our semi-empirical lifetime-intensity model in a fleet-wide analysis revealed additional aspects of the model that are important to consider when implementing the full distributions. Using the elasticity design with Normal distributions is simple and easy to understand but has a vital flaw when analyzing high driving intensities. As driving intensities increase, the distribution shifts to lower and even negative lifetimes, which of course is not realistic. This issue is overcome by instead using Weibull distributions, which is by definition always larger than zero. Hence, we have changed the elasticities used in the carbon footprint analysis to those estimated for the Weibull distribution and added the caveat of the Normal distribution to Table 1. Again, thank you for suggesting us to go beyond the one-vehicle perspective, which highlighted this important aspect of our lifetime-intensity model design.